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All Journal SIGMA: Jurnal Pendidikan Matematika PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jambura Journal of Mathematics METIK JURNAL Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Griya Journal of Mathematics Education and Application Research in the Mathematical and Natural Sciences Research Review: Jurnal Ilmiah Multidisiplin Prosiding Seminar Nasional Pendidikan Matematika, Universitas Mulawarman Pattimura International Journal of Mathematics (PIJMath) Jurnal Riset Mahasiswa Matematika The Indonesian Journal of Computer Science Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Computational and Applied Mathematics
Isran K Hasan
Universitas Negeri Gorontalo
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Pendekatan Hybrid VARIMA–ANN untuk Peramalan Multivariat Data Cuaca Bulanan di Provinsi Gorontalo Ali, Nur Anggraini T.; Wungguli, Djihad; Hasan, Isran K.
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37513

Abstract

Multivariate time series forecasting is essential for understanding the interrelationships among weather parameters. This study aims to develop a multivariate forecasting model using a hybrid Vector Autoregressive Integrated Moving Average (VARIMA)–Artificial Neural Network (ANN) approach with the backpropagation algorithm, applied to weather data from Gorontalo Province over the 2015–2023 period, including air temperature, humidity, and wind speed. The data were divided into training data (2015–2021) and testing data (2022–2023). The VARIMA model was employed to capture the linear component, while the residuals from the VARIMA model were subsequently modeled using ANN to capture nonlinear patterns. The order of the VARIMA model was determined based on the smallest Akaike Information Criterion (AIC) value, while model performance was evaluated using Mean Absolute Percentage Error (MAPE). The results indicate that the best-performing model is VARIMA(5,1,1)–ANN(18,9,3), with MAPE values of 1.32% for air temperature, 20.54% for humidity, and 21.96% for wind speed. These findings suggest that the hybrid VARIMA–ANN approach provides good forecasting performance and has the potential to serve as an alternative method for multivariate weather forecasting.   
IMPLEMENTASI WORD EMBEDDING GLOVE PADA SUPPORT VECTOR MACHINE DENGAN PARTICLE SWARM OPTIMIZATION UNTUK ANALISIS SENTIMEN Putu Ayu Indah Nazwa Usia; Isran K Hasan; Siti Nurmardia Abdussamad
SIGMA: JURNAL PENDIDIKAN MATEMATIKA Vol. 18 No. 1: Juni 2026
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/nnc0w522

Abstract

Tujuan: Penelitian ini menerapkan word embedding GloVe pada Support Vector Machine yang dioptimalkan memanfaatkan Particle Swarm Optimization untuk mengevaluasi efektivitas model klasifikasi analisis sentimen ulasan pengguna Indodax di platform media sosial X. Metode: Pada studi ini, data teks diklasifikasikan memanfaatkan pendekatan metodologi kuantitatif. Data berupa 877 cuitan pengguna Indodax di media sosial X yang dikumpulkan melalui teknik scraping menggunakan Python. Tahapan analisis meliputi pengumpulan data, pre-processing data, pelabelan sentimen, word embedding GloVe, pembagian data, Particle Swarm Optimization digunakan guna optimasi parameter, serta Support Vector Machine dimanfaatkan untuk klasifikasi. Hasil: Hasil penelitian menunjukkan bahwa analisis sentiment ulasan pengguna Indodax di media sosial X menghasilkan distribusi sentimen yang relatif seimbang, yaitu sebesar 49,7% sentimen positif dan 50,3% sentimen negatif. Selain itu, melalui skor akurasi sejumlah 82%, presisi 85%, recall 82%, serta f1-score 84%, hasil penilaian model menunjukkan bahwa penerapan word embedding GloVe pada SVM yang dioptimalkan dengan Particle Swarm Optimization dapat menghasilkan performa klasifikasi yang baik. Simpulan: Dengan menggabungkan GloVe word embedding dengan Support vector Machine yang dioptimasi menggunakan PSO, penelitian ini memberikan kontribusi dalam pengembangan metode analisis sentimen berbasis machine learning, serta memberikan implikasi praktis bagi pengelola platform dan pemerintah dalam memahami persepsi pengguna terhadap layanan investasi digital.
Implementasi Metode K-Medoids Clustering untuk Mengelompokkan Kecenderungan Menonton Drama Korea Suryahaty Aisyah Aulia Kaluku; Muhammad Rezky Friesta Payu; Isran K. Hasan
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1066

Abstract

The phenomenon of watching Korean dramas is increasingly growing among female university students and has the potential to create differences in viewing behavior patterns with varying levels of intensity. This study aims to determine the optimal number of clusters using the Elbow method and to identify respondent grouping patterns based on similarities in Korean drama viewing behavior using the K-Medoids method. In addition, this study evaluates the quality of the formed clusters using an internal validation method, namely the Silhouette Coefficient. The data used are primary data obtained through the distribution of questionnaires to female students at Universitas Negeri Gorontalo, incorporating aspects that represent the intensity and tendencies of Korean drama viewing behavior. The analysis begins with determining the number of clusters using the Elbow method based on changes in the Sum of Squared Errors (SSE). The results show that 4 clusters represent the most representative number of clusters visually. Furthermore, the K-Medoids method is applied to group respondents into 4 clusters based on similarities in their Korean drama viewing behavior. However, the evaluation results using the Silhouette Coefficient indicate that the quality of the formed clusters tends to be low. This is reflected in the variation of silhouette coefficient values, where only one cluster demonstrates good clustering quality, while the others exhibit weaker structures with unclear separation between clusters. This condition indicates the presence of data overlap among clusters, resulting in less distinct cluster boundaries
Penerapan Metode Fuzzy Time Series Chen Orde Tinggi Pada Peramalan Nilai Tukar Petani Provinsi Gorontalo Nur Miftah Muhammad; Isran K. Hasan; Armayani Arsal; Emli Rahmi; Laode Nashar
Jurnal Riset Mahasiswa Matematika Vol 5, No 4 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i4.41337

Abstract

Nilai Tukar Petani (NTP) merupakan salah satu indikator ekonomi yang digunakan untuk menggambarkan tingkat kesejahteraan petani dan kondisi sektor pertanian. Pergerakan nilai NTP yang bersifat fluktuatif memerlukan pendekatan peramalan yang mampu menangkap pola data secara memadai. Penelitian ini bertujuan menerapkan metode Fuzzy Time Series (FTS) Chen orde tinggi untuk meramalkan Nilai Tukar Petani di Provinsi Gorontalo serta mengidentifikasi model orde yang memberikan tingkat kesalahan peramalan yang paling tepat. Data yang digunakan berupa data bulanan NTP Provinsi Gorontalo periode Januari 2020 hingga Oktober 2025 yang terdiri dari 70 observasi dan diperoleh dari publikasi resmi Badan Pusat Statistik. Data dibagi menjadi 80% data latih dan 20% data uji menggunakan pendekatan pembagian berdasarkan waktu. Tahapan analisis meliputi penentuan himpunan semesta, pembentukan interval, proses fuzzifikasi, pembentukan Fuzzy Logical Relationship (FLR) dan Fuzzy Logical Relationship Group (FLRG), defuzzifikasi, serta evaluasi kinerja model menggunakan Mean Absolute Percentage Error (MAPE). Hasil analisis menunjukkan bahwa model FTS Chen orde dua menghasilkan nilai MAPE sebesar 3,3164% pada data uji, yang lebih kecil dibandingkan dengan model orde satu. Sementara itu, model orde tiga tidak dapat digunakan secara optimal karena tidak terbentuk hubungan fuzzy pada beberapa periode data pengujian. Hasil ini menunjukkan bahwa pendekatan FTS Chen orde dua dapat memberikan hasil peramalan yang relatif lebih baik pada data NTP yang dianalisis dalam penelitian ini.
Perbandingan Metode ARFIMA dan Metode ARIMA-FFNN (Studi Kasus: Harga Saham di PT. Telekomunikasi Indonesia Tbk) Afandi W. Biga; Isran K. Hasan; Nurwan
Research Review: Jurnal Ilmiah Multidisiplin Vol. 4 No. 2 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Agustus 2025 - Januari 2026)
Publisher : Transbahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54923/researchreview.v4i2.221

Abstract

This study aims to compare the effectiveness of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model and the Autoregressive Integrated Moving Average–Feedforward Neural Network (ARIMA-FFNN) hybrid model in forecasting the stock price of PT Telekomunikasi Indonesia Tbk. Forecasting stock prices is a crucial aspect of financial decision-making since accurate predictions can support investors and policymakers in minimizing risks and maximizing returns. In this study, the ARFIMA(1,d,1) model and the ARIMA(0,d,2)-FFNN(0,2) hybrid model were applied to historical daily stock price data of PT Telekomunikasi Indonesia Tbk. The performance of both models was evaluated using the Mean Absolute Percentage Error (MAPE), which is widely recognized as a reliable metric for measuring prediction accuracy. The results revealed that the ARFIMA(1,d,1) model generated a MAPE value of 2.11%, while the ARIMA(0,d,2)-FFNN(0,2) model achieved a significantly lower MAPE value of 1.28%. These findings indicate that the hybrid ARIMA-FFNN approach provides more accurate forecasting results compared to the ARFIMA model. Therefore, the ARIMA(0,d,2)-FFNN(0,2) model can be considered a more optimal and reliable forecasting method for predicting stock prices in PT Telekomunikasi Indonesia Tbk. The results of this study highlight the potential of combining traditional time series models with machine learning approaches to enhance forecasting accuracy in financial markets.
Performance Analysis of the Random Forest and Logistic Regression Models for Predicting the Effect of the Environment on Land Value Zones (ZNT) in Gorontalo City Irnawati Dau; Isran K. Hasan; Armayani Arsal
Jurnal Riset Mahasiswa Matematika Vol 5, No 5 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i5.43715

Abstract

Zona Nilai Tanah (ZNT) merupakan indikator penting nilai ekonomi regional dan dipengaruhi oleh berbagai faktor lingkungan dan spasial. Studi ini bertujuan untuk membandingkan kinerja model Regresi Logistik Ordinal dan Random Forest dalam memprediksi klasifikasi ZNT di Kota Gorontalo. Studi ini menggunakan data spasial sekunder yang berasal dari peta ZNT tahun 2023 dan 2024, yang terdiri dari 560 sampel. Variabel prediktor meliputi topografi, jarak ke sumber daya alam, ruang terbuka hijau, fasilitas umum, sumber polusi, dan penggunaan lahan, sedangkan variabel respons dikategorikan ke dalam kelas ZNT rendah, menengah, dan tinggi.Analisis ini melibatkan pra-pemrosesan data, transformasi variabel, dan pembagian data menjadi set pelatihan dan pengujian dengan rasio 80:20. Kedua model dievaluasi menggunakan matriks kebingungan untuk menilai kinerja klasifikasi.  Hasil penelitian menunjukkan bahwa model Regresi Logistik Ordinal dan Random Forest berkinerja sama baiknya dalam mengklasifikasikan Zona Nilai Tanah (LVZ), dengan akurasi tinggi pada data uji. Selain itu, penggunaan lahan dan kedekatan dengan fasilitas umum merupakan faktor yang paling berpengaruh dalam klasifikasi LVZ.Studi ini menunjukkan bahwa pendekatan statistik dan pembelajaran mesin dapat memberikan prediksi yang akurat dalam analisis nilai lahan berdasarkan faktor lingkungan. Regresi logistik ordinal unggul dalam menafsirkan hubungan antar variabel melalui pengujian statistik, sedangkan Random Forest lebih efektif dalam mengidentifikasi pentingnya variabel melalui analisis kepentingan fitur. Hasil studi ini diharapkan dapat menjadi referensi untuk perencanaan spasial dan pembuatan kebijakan terkait pengelolaan nilai lahan di Kota Gorontalo.
COMPARISON OF PSO AND ABC IN CHENG FUZZY TIME SERIES FOR RICE PRICE FORECASTING Machdina Indira Laupa; Isran K. Hasan; Nisky Imansyah Yahya
Jurnal Statistika dan Aplikasinya Vol. 10 No. 1 (2026): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.10102

Abstract

Rice prices as a primary food commodity in Indonesia play an important role in maintaining economic stability and public welfare, but tend to fluctuate, thus requiring accurate forecasting methods to support decision-making. Research on optimization in the Cheng Fuzzy Time Series (FTS Cheng) method remains limited, particularly in comparing the performance of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) in rice price forecasting. This study aims to compare the performance of PSO and ABC optimization in the FTS Cheng method using monthly data from January 2018 to October 2025, with accuracy evaluated using MAE, RMSE, and MAPE. The forecasting process is carried out through interval formation on training and testing data to obtain an optimal model. The results show that FTS Cheng-ABC performs better, with an MAE of 97.947, RMSE of 142.855, and MAPE of 0.633%, compared to FTS Cheng-PSO with an MAE of 118.579, RMSE of 153.354, and MAPE of 0.767%. However, this study is limited to the use of the Fuzzy Time Series Cheng method with two optimization algorithms, namely PSO and ABC, and does not incorporate adaptive parameter mechanisms or comparisons with more advanced methods. Therefore, the FTS Cheng-ABC method is more effective and can be used to support policy decision-making related to rice price stability. This study contributes by providing a comparative analysis of PSO and ABC optimization in improving the performance of the FTS Cheng method for rice price forecasting in Indonesia.
Co-Authors Abubakar, Agung Sucipto Afandi W. Biga Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Ali, Nur Anggraini T. Alvitha Habibie Armayani Arsal Armayani Arsal Asriadi Asriadi Asriadi Asriadi Buhungo, Rayhanul Jannah Burhan, Dewinto Demas Novaleda Abdul Karim Dewi Rahmawaty Isa Djihad Wungguli Djuma, Clara Anggriani Doholio, Nadya Pratiwi Emiro, Awalia Harmain, Ismail Saputra R. Harun, Trieke Nurfadilah Hasan S. Panigoro HASIRU, LISA SYAHRIA Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Indrawati Lihawa Irnawati Dau Ismail Djakaria Ivan Novri Jefriyanto Ibrahim Karim, Finansiya S. Abd. Kasim, Afrianto Pratama Khairun Nisa Humolungo Kilo, Nur Ain Kurniawan Kurniawan La Ode Nashar Lailany Yahya Laode Nashar Lapitung, Putri Machdina Indira Laupa Mohamad, Ramla Mokodompit, Marcela Nadhilah, Farhah Nanda Arista Rizki NISKY IMANSYAH YAHYA Novianita Achmad Nur Falaq Nur Miftah Muhammad Nur Siyam Djibu Nurdin, Sri Ayu Nurul Afifah Nurwan Nurwan Nurwan, Nurwan Pakaya, Desya Neydi Putri Pomahiya, Saiful Putu Ayu Indah Nazwa Usia Rahmi, Emli Resmawan Resmawan Salmun K. Nasib Sampe, Nofia Sigar, Tesya Siti Nurmardia Abdussamad Siti Nurmardia Abdussamad Suci Tilome Suharmin, Wandayana Nur'Amanah Sumarno Ismail Suryahaty Aisyah Aulia Kaluku Thesalonica Graina Barung Ulopo, Asrul S Usman, Rahmat Setiawan Wewengkang, Hanz Franklyn Bachruddin Winni A. Pakaya wolah, Meitasya